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Asian Pacific Journal of Cancer Prevention : APJCP logoLink to Asian Pacific Journal of Cancer Prevention : APJCP
. 2021 Nov;22(11):3419–3431. doi: 10.31557/APJCP.2021.22.11.3419

A Meta-Analysis for Association of XRCC3 rs861539, MTHFR rs1801133, IL-6 rs1800795, IL-12B rs3212227, TNF-α rs1800629, and TLR9 rs352140 Polymorphisms with Susceptibility to Cervical Carcinoma

Seyedeh Fatemeh Parsaeian 1, Fatemeh Asadian 2,*, Mojgan Karimi-Zarchi 3,4, Sepideh Setayesh 5, Atiyeh Javaheri 1, Razieh Sadat Tabatabaie 1, Seyed Alireza Dastgheib 6, Hossein Golestanpour 7,8, Hossein Neamatzadeh 9,10
PMCID: PMC9068191  PMID: 34837895

Abstract

Background:

In spite of substantial declines in both incidence and mortality rates in the past 50 years, cervical cancer remains one of the leading causes of cancer associated mortality among women globally. We performed this meta-analysis to explore the role of XRCC3 rs861539, MTHFR rs1801133, IL-6 rs1800795, IL-12B rs3212227, TNF-α rs1800629 and TLR9 rs352140 polymorphism with susceptibility to cervical carcinoma.

Methods:

The search databases include PubMed, SciELO, MedRxiv, Web of Science, Scopus, Cochrane Library, China National Knowledge Infrastructure, and China Biology Medicine disc up to 30 June 2021. The language is limited to English and Chinese. The comparison between the polymorphisms and cervical cancer was assessed using pooled odds ratio (OR) and 95% confidence interval (CI). The data are statistically analyzed by Comprehensive Meta-Analysis (CMA) 2.0 software.

Results:

A total of 59 studies including seven studies with 1,112 cases and 1,233 controls on XRCC3 rs861539, 14 studies with 2,694 cases and 3349 controls MTHFR rs1801133, four studies with 1,121 cases and 1,109 controls on IL-12B rs3212227, seven studies with 1,452 cases and 2,186 controls on IL-6 rs1800795, 20 studies with 4,781 cases and 4909 controls on TNF-α rs1800629, and seven studies with 1743 cases and 2292 controls on TLR9 rs352140 were included. There was a significant association between XRCC3 RS861539, TNF-α rs1800629, and IL-6 rs1800795 polymorphisms and an increased risk of cervical carcinoma in overall population. However, the MTHFR rs1801133, IL-12B rs3212227 and TLR9 rs352140 polymorphisms were not associated.

Conclusion:

The pooled analysis showed that XRCC3 RS861539, TNF-α rs1800629, and IL-6 rs1800795 were associated with cervical carcinoma susceptibility, but not MTHFR rs1801133, IL-12B rs3212227 and TLR9 rs352140 polymorphisms.

Key Words: Cervical carcinoma, cervical cancer, meta-analysis, gene, polymorphism

Introduction

Cancer is one of the main public health problems globally with about 18.1 million new cancer cases and 9.6 million cancer deaths in 2018 (Ghelmani et al., 2021; Jarahzadeh et al., 2021; Antikchi et al., 2021). The World Health Organization (WHO) states cervical cancer is the second most frequent cancer among women worldwide (Hamadani et al., 2017; Da Silva et al., 2021) with an estimated 570,000 new cases and 311,000 deaths in 2018 worldwide (Yi et al., 2020). It is reported that 85% of cervical cancer cases and 87% of the cervical cancer deaths occur in less developed countries (WHO/ICO, 2010). With over 500,000 cases of cervical cancer reported each year, nearly 80 percent of those are in developing countries, including Africa with 68,000, an estimated 77,000 in Latin America and the Caribbean, and 245,000 in Asia (Arbyn et al., 2020). Cervical cancer is driven by persistent infection with one of 15 carcinogenic human papillomavirus (HPV) types (Ghaemmaghamiet al., 2008; Chen et al., 2011). However, the impact of HPV type and intratypic variants on patient outcomes is far less understood (Karimi-Zarchi et al., 2013; Baghestani et al., 2018; Rader et al., 2019).

To date, the occurrence and development of cervical cancer is suggested to be associated with persistent HPV infection (Ghaemmaghami et al., 2008; Zarchi et al., 2010; Pal and Kundu, 2020). The DNA of HPV integrates into the host cell genome and disrupts the open reading frame and causes overexpression of E6 and E7 genes (Binesh et al., 2012; Soheili et al., 2016). However, the specific molecular mechanisms and potential single gene require further studies. Familial based studies and evaluation of inherited genetic variations revealed that host genetic factors have a role in cervical cancer pathogenesis (Yang et al., 2020; Ahmadi et al., 2021). Recently, a comprehensive review study showed that CDK1, CCNB1, ITGB1, FN1, MMP9 and STAT1 played different roles in the progression of cervical cancer through different signaling pathways (Sayad, Ahmadi, Nekouian, et al., 2020; Yi et al., 2020). Moreover, studies have shown that cervical cancer has a heritable genetic component. However, as other complex disease, the identified loci only explain a minority of the risk of cervical cancer. This gap of the heritability explained by genetic markers genome-wide association studies and the heritability identified in familial studies has been termed as ‘missing heritability’ (Chen et al., 2016; Leo et al., 2017; Sayad, Ahmadi, Moradi, et al., 2020). Thus, our understanding of the genetic basis of cervical cancer is still limited. In this meta-analysis, we explore the role of XRCC3 rs861539, MTHFR rs1801133, IL-6 rs1800795, IL-12B rs3212227, TNF-α rs1800629, and TLR9 rs352140 polymorphisms in susceptibility to cervical cancer.

Materials and Methods

Literate Search Strategy

We performed a comprehensive literature search on PubMed, Scopus, China National Knowledge Infrastructure, Wanfang, VIP Information Chinese Journal Service Platform, and China Biology Medicine disc, PubMed, EMBASE, Web of Science and the Cochrane Library, Google Scholar, Cochrane Library, EMBASE, Scientific Information Database (SID), WanFang, VIP, Chinese Biomedical Database (CBD), Scientific Electronic Library Online (SciELO), China National Knowledge Infrastructure (CNKI), IranDoc and Egyptian Knowledge Bank (EKB) Journals database to identify all relevant studies on the association of XRCC3 rs861539, MTHFR rs1801133, IL-6 rs1800795, IL-12B rs3212227, TNF-α rs1800629 and TLR9 rs352140 with cervical cancer up to 30th June 2021. The following terms were used in various combinations in this search: (‘’Uterine Cervical Neoplasm‘’ OR ‘’Cervix Cancer’’ OR ‘’Cervical Cancer’’ OR ‘’Cervical Neoplasm’’ OR ‘’Cervical Carcinoma’’ OR ‘’Squamous Cell Carcinoma’’ OR ‘’Adenocarcinoma’’) AND (‘’X-Ray Repair Cross Complementing 3’’ OR ‘’XRCC3’’ OR ‘’rs861539’’ OR ‘’g.4880C>G’’ OR ‘’c.-237C>G’’ OR ‘’n.54-321G>C’’) AND (‘’Methylene tetrahydrofolate reductase’’ OR ‘’MTHFR’’ OR ‘’677C>T’’ OR ‘’rs1801133’’ OR ‘’g.14783C>T’’ OR ‘’c.788C>T’’ OR ‘’p.Ala263Val’’ OR ‘’p.Ala222Val’’) AND (‘’Interleukin 12’’ OR ‘’IL-12’’ OR ‘’1188A>C’’ OR ‘’rs3212227’’ OR ‘’ g.158742950T>G’’ OR ‘’ g.19532A>C’’ OR ‘’ c.*159A>C’’) AND (‘Interleukin 6’’ OR ‘’IL-6’’ OR ‘’-174G>C’’ OR ‘’rs1800795’’ OR ‘’ g.4880C>G’’ OR ‘’c.-274C>G’’ OR ‘’ n.54-321G>C’’) AND (‘’Toll like receptors’’ OR ‘’TLR’’ ‘’ OR ‘’rs352140’’ OR ‘’2848G/A’’) AND (‘’Gene’’ OR ‘’Single-Nucleotide Polymorphism” or “SNP” OR ‘’Polymorphism’’ OR ‘’Genotype’ OR ‘’Allele’’ OR ‘’Variant’’ OR ‘’Variation’’ OR ‘’Mutation’’ OR ‘’Mutant’’). The search was carried out in English, Chinese and Persian. When overlapping data on the same cases were included in more than one publication, only the one with the larger sample size was selected. Moreover, the reference list of the retrieved studies and reviews manually checked to identify more potential eligible studies.

Eligibility criteria

To obtain the papers, the following criteria should be met to include the papers in our study: 1) studies with case-control or cohort design; 2) studies reported original data; 3) studies appraised association of XRCC3 rs861539, MTHFR rs1801133, IL-6 rs1800795, IL-12B rs3212227, TNF-α rs1800629 and TLR9 rs352140 polymorphisms with cervical cancer; and 4) studies with available and sufficient data for calculating an odds ratio (OR) with 95% confidence interval (CI). The following exclusion criteria were also used: 1) studies on other polymorphism at XRCC3, MTHFR, IL-12B, IL-6, and TNF-α genes; 2) animal studies or in vitro studies; 3) studies with in sufficient data on genotype frequencies or which the number of genotypes and alleles could not be ascertained; 4) linkage studies; 5) family based studies including sibling, twins and trios-parents studies; 6) abstracts, case reports, commentaries, editorials, conference articles, reviews, proceedings and meta-analyses; and 7) duplicates studies or overlapping data.

Data Extraction

Two authors reviewed and extracted necessary information independently in accordance with our inclusion criteria. For conflicting data, the authors carried out discussions until a consensus was reached. If they could not reach a consensus, disagreement was resolved by the third author who participated in the discussion. For each eligible study the following data was collected: first author name, year of publication, country of origin, ethnicity (Caucasian, Asian, African, Mixed populations), genotyping methods, sample size, allele and genotype frequency of XRCC3 rs861539, MTHFR rs1801133, IL-12B rs3212227, IL-6 rs1800795, TNF-α rs1800629 and TLR9 rs352140 polymorphisms in cervical cancer cases and controls, Minor Allele Frequency (MAFs) and Hardy-Weinberg equilibrium (HWE) in healthy controls. In this meta-analysis different case-control groups or cohorts in one publication were considered as independent studies. We did not define any minimum sample size to include in this meta-analysis. The ‘‘mixed’’ group means mixed or unknown populations. If more than one study was published by the same author(s) using repeated or overlapped data, the studies with the largest sample size or the most recently published study was included to the meta-analysis. If selected articles did not reported necessary data the corresponding authors was contacted by email to request the missing data.

Statistical Analysis

The comparison between the XRCC3 rs861539, MTHFR rs1801133, IL-6 rs1800795, IL-12B rs3212227, TNF-α rs1800629 and TLR9 rs352140 polymorphism and cervical cancer was assessed using pooled odds ratio (OR) and 95% confidence interval (CI). The pooled data were calculated under five genetic models, i.e., allele (B vs. A), homozygote (BB vs. AA), heterozygote (BA vs. BB), dominant (BB+BA vs. AA) and recessive (BB vs. BA+AA), in which a ‘‘A’’ denotes a major allele; ‘‘B’’ denotes a minor allele. The χ2 test and I2 statistics were used to assess whether there was between-study heterogeneity, in which P >.10 and I2 < 50% could be considered that there was no statistical heterogeneity between the research results. The fixed effect model was selected for data consolidation; P ≤.10 and I2 ≥50% could be considered that there was statistical heterogeneity between the research results, and a random effect model was used for data consolidation. If there was significant heterogeneity among studies, the random effects model (DerSimonian and Laird) was used; otherwise, the fixed-effects model (Mantel and Haenszel) was acceptable. The Hardy-Weinberg equilibrium of the control group was evaluated using the χ2 test, and the expected and actual genotype frequencies of the control group were compared. In this meta-analysis, P-values of ≤.05 were considered statistically significant (Azadi-Yazdi et al., 2017; Mirjalili et al., 2019; Dastgheib et al., 2020). In addition, the sensitivity analysis was performed by excluding HWE-violating studies. Potential publication bias was evaluated using the Egger’s linear regression test and Begg’s quantitative test. The asymmetric plot of Egger’s test and the P-value of Begg’s test less than 0.05 were considered a significant publication bias. If there was evidence of publication bias (P<0.05), trim and fill method was applied to adjust for the effect of publication bias (Jafari et al., 2020). All statistical analyses were performed using Comprehensive Meta-Analysis (CMA) Software version 2.0 (Biostat, Englewood, USA). All tests were two-sided, and the P< 0.05 was considered statistically significant.

Results

Characteristics of the Enrolled Studies

The flowchart of selection of studies and reasons for exclusion is presented in Figure 1. There were 1,013 articles relevant to our search words and 395 duplicated studies were excluded. Then, 207 studies removed after reading titles and abstracts. Following the inclusion exclusion criteria 352 studies were excluded. Finally, a total of 59 studies were included in this meta-analysis. There are seven studies with 1112 cases and 1233 controls on XRCC3 rs861539 (He et al., 2008; Xiao, 2009; Settheetham-Ishida et al., 2011; Djansugurova et al., 2013; Pérez et al., 2013; Al-Harbi et al., 2017), 14 studies with 2694 cases and 3349 controls MTHFR rs1801133 (Lambropoulos et al., 2003; Sull et al., 2004; Kang et al., 2005; Zoodsma et al., 2005; Delgado-Enciso et al., 2006; Ma et al., 2006; Nandan et al., 2008; Shekari et al., 2008; Kohaar et al., 2010; Yang et al., 2011; Prasad and Wilkhoo, 2011; Tong et al., 2011; Mostowska et al., 2011; von Keyserling et al., 2011), four studies with 1121 cases and 1109 controls on IL-12B rs3212227 (Chen et al., 2009; Do Carmo Vasconcelos De Carvalho et al., 2012; Roszak, Mostowska, et al., 2012), seven studies with 1452 cases and 2186 controls on IL-6 rs1800795 (Nogueira De Souza et al., 2006; Gangwar et al., 2009; Grimm et al., 2011; Shi et al., 2013; de Lima Júnior et al., 2016; Pu et al., 2016; Zidi et al., 2017), 20 studies with 4,781 cases and 4909 controls on TNF-α rs1800629 (Stanczuk et al., 2019; Jang et al., 2001; Calhoun et al., 2002; Gostout et al., 2003; Deshpande et al., 2005; Duarte et al., 2005; Govan et al., 2006; Kohaar et al., 2007; Singh et al., 2009; Zu et al., 2010; Ivansson et al., 2010; Zuo et al., 2011; Wang et al., 2011, 2012; Badano et al., 2012; Barbisan et al., 2012; Sousa et al., 2014; Roszak et al., 2015; Zidi et al., 2015) and seven studies with 1743 cases and 2292 controls on TLR9 rs352140 (Pandey et al., 2011; Roszak, Lianeri, et al., 2012; Lai et al., 2013; Bi, 2014; Zidi et al., 2016; Jin et al., 2017). The details of included studies were shown in Tables 1 and 2. All those 22 studies were reported in English and Chinese. Among those 59 studies, 31 studies were from Asian populations, 18 studies from Caucasian populations, five studies from African populations, and five studies were from mixed populations. The studies have been carried out in China, Thailand, Argentina, Kazakhstan, Brazil, Saudi Arabia, Greece, Korea, Netherlands, Mexico, India, Poland, Germany, Austria, Tunisia, Zimbabwe, USA, South Africa, Sweden, and Portugal. The sample size of cases ranged from 21 to 636, while the sample size of controls ranged from 73 to 800 in the controls. Eight different methods including AS-PCR, PCR-RFLP, Direct Sequencing, SnapShot, TaqMan, LDR-PCR, ARMS-PCR, qRT-PCR and HMR were used to genotyping those polymorphisms. Hardy-Weinberg equilibrium (HWE) test was calculated for all publications and P<0.05 was considered as a departure from HWE (Table 1).

Table 1.

Characteristics of Studies Included in the Meta-Analysis

First Author/Year Country SOC Genotyping Case/Control Cases Controls MAFs HWE
(Ethnicity) Technique Genotypes Allele Genotypes Allele
XRCC3 rs861539 CC CT TT C T CC CT TT C T
He 2008 China(Asian) PB AS-PCR 200/200 177 19 4 373 27 182 17 1 381 19 0.047 0.391
Xiao 2010 China(Asian) PB PCR-RFLP 158/164 82 59 17 223 93 115 41 8 271 57 0.173 0.097
Settheetham-Ishida 2011 Thailand(Asian) PB PCR-RFLP 111/118 101 10 0 212 10 106 12 0 224 12 0.05 0.56
Pérez 2013 Argentina(Caucasian) PB Sequencing 117/205 50 56 11 156 78 78 95 32 251 159 0.387 0.73
Djansugurova 2013 Kazakhstan(Caucasian) PB AS-PCR 217/160 140 57 20 337 97 124 32 4 280 40 0.125 0.278
Colacino-Silva 2017 Brazil(Mixed) HB PCR-RFLP 77/73 43 28 6 114 40 36 30 7 102 44 0.301 0.837
Al-Harbi 2017 Saudi Arabia(Asian) NA PCR-RFLP 232/313 79 126 27 284 180 126 145 42 397 229 0.365 0.977
MTHFR rs1801133 CC CT TT C T CC CT TT C T
Lambropoulos 2003 Greece(Caucasian) HB PCR-PFLP 21/91 11 8 2 30 12 42 37 12 121 61 0.335 0.403
Sull 2004 Korea(Asian) HB SNapShot 246/454 73 115 58 261 231 153 221 80 527 381 0.42 0.989
Kang 2005 Korea(Asian) HB PCR-PFLP 79/74 27 32 20 86 72 30 32 12 92 56 0.378 0.487
Zoodsma 2005 Netherlands(Caucasian) HB TaqMan 636/592 357 230 49 944 328 273 262 57 808 376 0.318 0.608
Ma 2006 China(Asian) HB PCR-PFLP 111/111 20 53 38 93 129 33 60 18 126 96 0.432 0.286
Delgado 2006 Mexico(Mixed) HB PCR-PFLP 70/89 18 34 14 70 62 20 49 20 89 69 0.5 0.34
Nandan 2008 India(Asian) HB PCR-PFLP 62/77 36 0 26 72 52 53 0 24 106 48 0.312 ≤0.001
Shekari 2008 India(Asian) HB PCR-PFLP 200/200 125 68 7 318 82 170 28 2 368 32 0.08 0.489
Kohaar 2010 India(Asian) HB SNapShot 164/231 113 47 4 273 55 161 65 5 387 75 0.162 0.598
Yang 2010 China(Asian) HB PCR-PFLP 391/382 229 85 77 530 234 182 166 34 536 234 0.306 0.658
Mostowska 2011 Poland(Caucasian) HB PCR-PFLP 124/168 56 59 9 171 77 69 81 18 219 117 0.348 0.42
Prasad 2011 India(Asian) PB PCR-PFLP 62/125 57 5 0 119 5 116 8 1 240 10 0.04 0.062
Tong 2011 Korea(Asian) HB TaqMan 146/427 53 65 28 171 121 152 198 77 502 342 0.412 0.373
Keyserling 2011 Germany(Caucasian) HB LDR-PCR 386/328 164 188 34 516 256 165 136 27 466 190 0.29 0.89
IL-12B rs3212227 AA AC CC A C AA AC CC A C
Han 2008 Korea(Asian) HB SNaPShot 150/179 32 87 31 151 149 52 88 39 192 166 0.464 0.877
Chen 2009 China(Asian) PB PCR-RFLP 404/404 127 199 78 453 355 150 185 69 485 323 0.4 0.357
de Carvalho 2012 Brazil(Mixed) PB PCR-RFLP 162/76 100 49 13 249 75 31 37 8 99 53 0.349 0.531
Roszak 2012 Poland(Caucasian) PB PCR-RFLP 405/450 212 174 19 598 212 289 151 10 729 171 0.19 0.055
IL-6 rs1800795  GG GC CC G C GG GC CC G C
de Souza 2006 Brazil(Mixed) PB PCR-RFLP 56/253 24 32 0 80 32 148 102 3 398 108 0.213 0.001
IL-6 rs1800795  GG GC CC G C GG GC CC G C
Gangwar 2009 India(Asian) HB ARMS-PCR 160/200 107 36 17 250 70 142 51 7 335 65 0.163 0.371
Grimm 2011 Austria(Caucasian) HB Sequencing 131/208 55 51 25 161 101 85 96 27 266 150 0.361 0.989
Junior 2012 Brazil(Mixed) PB Sequencing 115/115 72 39 4 183 47 67 37 11 171 59 0.257 0.093
Shi 2014 China(Asian) HB PCR-RFLP 518/518 160 253 105 573 463 181 259 78 621 415 0.401 0.348
Pu 2016 China(Asian) HB TaqMan 360/728 185 141 34 511 209 476 220 32 1172 284 0.195 0.309
Zidi 2017 Tunisia(African) HB qRT-PCR 112/164 81 25 6 187 37 133 25 6 291 37 0.113 0.002
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Table 2.

Characteristics of the Case-Control Studies Included in the Meta-Analyses

First Author Country SOC Genotyping Case/Control Cases Controls MAFs HWE
Ethnicity Method Genotype Allele Genotype Allele
TNF-α rs1800629  GG AG AA G A GG AG AA G A
Jang 2001 Korea(Asian) PB PCR-RFLP 51/92 46 3 2 95 7 85 7 0 177 7 0.038 0.704
Calhoun 2002 USA(Caucasian) HB Sequencing 127/107 91 27 9 209 45 73 30 4 176 38 0.177 0.678
Stanczuk 2003 Zimbabwe(African) PB ARMS-PCR 103/101 74 28 1 176 30 81 18 2 180 22 0.108 0.41
Gostout 2003 USA(Caucasian) HB Sequencing 127/175 91 27 9 209 45 117 53 5 287 63 0.18 0.731
Duarte 2005 Portugal(Caucasian) PB PCR-RFLP 195/244 138 50 7 326 64 200 40 4 440 48 0.098 0.236
Deshpande 2005 USA(Caucasian) HB Sequencing 258/411 188 54 16 430 86 297 100 14 694 128 0.155 0.13
Govan 2006 South Africa(African) HB ARMS-PCR 244/228 174 62 8 410 78 172 46 10 390 66 0.144 0.005
Kohaar 2007 India(Asian) HB PCR-RFLP 120/165 94 22 4 210 30 150 15 0 315 15 0.045 0.54
Wang 2009 China(Asian) PB TaqMan 456/800 386 67 3 839 73 666 126 8 1458 142 0.088 0.457
Singh 2009 India(Asian) HB PCR-RFLP 150/162 122 17 11 261 39 147 11 4 305 19 0.058 ≤0.001
Ivansson 2010 Sweden(Caucasian) PB TaqMan 1263/552 891 340 32 2122 404 396 138 18 930 174 0.157 0.169
Zu 2010 China(Asian) HB PCR 83/91 30 50 3 110 56 66 16 9 148 34 0.186 ≤0.001
Wang 2011 China(Asian) PB PCR 186/200 149 30 7 328 44 144 46 10 334 66 0.165 0.019
Zuo 2011 China(Asian) HB PCR-RFLP 239/110 158 81 0 397 81 83 25 2 191 29 0.131 0.941
Wang 2012 China(Asian) HB PCR-RFLP 285/318 247 30 8 524 46 274 35 9 583 53 0.083 ≤0.001
Barbisan 2012 Argentina(Caucasian) HB PCR-RFLP 122/176 87 32 3 206 38 126 46 4 298 54 0.153 0.483
Badano 2012 Argentina(Caucasian) HB Sequencing 56/113 44 10 2 98 14 101 12 0 214 12 0.053 0.551
Sousa 2014 Portugal(Caucasian) PB TaqMan 223/205 152 65 6 369 77 164 39 2 367 43 0.104 0.849
Zidi 2014 Tunisia(African) PB ARMS-PCR 130/260 55 33 43 143 117 141 35 84 317 203 0.39 ≤0.001
Roszak 2015 Poland(Caucasian) HB HMR 362/399 217 123 22 557 167 263 125 11 651 147 0.184 0.397
TLR9 rs352140 GG GA AA G A GG GA AA G A
Pandey 2011 India(Asian) PB PCR-RFLP 200/200 59 115 26 233 167 59 112 29 230 170 0.425 0.039
Roszak 2012 Poland(Caucasian) PB PCR-RFLP 426/460 87 230 109 404 448 122 235 103 479 441 0.479 0.614
Lai 2013 China(Asian) HB PCR-RFLP 120/100 98 14 8 210 30 97 2 1 196 4 0.02 ≤0.001
Bi 2014 China(Asian) PB PCR-RFLP 102/100 33 58 11 124 80 31 47 22 109 91 0.455 0.6
Zidi 2016 Tunisia(African) PB PCR-RFLP 130/260 42 48 40 132 128 83 117 60 283 237 0.456 0.134
Jin 2017 China(Asian) HB PCR-RFLP 420/842 208 160 52 576 264 543 257 42 1343 341 0.202 0.11
Xu 2017 China(Asian) PB TaqMan 345/330 135 163 47 433 257 131 152 47 414 246 0.373 0.785
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Quantitative Data Synthesis

XRCC3 RS861539

The summary for the association of XRCC3 RS861539 polymorphism with cervical cancer risk are shown in Table 3. Pooled data revealed that XRCC3 RS861539 polymorphism was significantly associated with susceptibility to cervical cancer under the heterozygote genetic model (TC vs. CC: OR = 1.00, 95% CI 1.066-1.585, p = 0.009). Moreover, stratified analysis by ethnicity revealed that the polymorphisms was significantly associated with cervical cancer in Asian women under three genetic models, i.e., allele (T vs. C: OR= 1.302, 95% CI 1.076-1.576, P= 0.007), heterozygote (TC vs. CC: OR = 1.441, 95% CI 1.113-1.867, p = 0.006) and dominant (TT+TC vs. CC: OR = 1.469, 95% CI 1.148-1.880, p = 0.002), but not in Caucasian women.

Table 3.

Results of the Association of XRCC3 RS861539, MTHFR rs1801133 and IL-12B rs3212227 Polymorphisms with Cervical Cancer Risk

Subgroup Genetic Model Type of Model Heterogeneity Odds Ratio Publication Bias
I2 (%) PH OR 95% CI Ztest POR PBeggs PEggers
XRCC3 rs861539
Overall T vs. C Random 73.82 0.001 1.223 0.897-1.669 1.272 0.203 1 0.901
TT vs. CC Random 68.44 0.007 1.456 0.723-2.932 1.053 0.292 0.707 0.376
TC vs. CC Fixed 26.84 0.224 1.3 1.066-1.585 2.596 0.009 0.548 0.242
TT+TC vs. CC Random 58.55 0.025 1.27 0.935-1.726 1.53 0.126 0.763 0.452
TT vs. TC+CC Random 64.54 0.015 1.309 0.693-2.470 0.829 0.407 0.452 0.225
Asians T vs. C Fixed 60.39 0.056 1.302 1.076-1.576 2.716 0.007 1 0.862
TT vs. CC Fixed 58.94 0.088 1.457 0.918-2.314 1.595 0.111 1 0.446
TC vs. CC Fixed 14.62 0.319 1.441 1.113-1.867 2.768 0.006 0.308 0.474
TT+TC vs. CC Fixed 36.18 0.195 1.469 1.148-1.880 3.055 0.002 0.734 0.666
TT vs. TC+CC Fixed 61.31 0.075 1.165 0.754-1.801 0.689 0.491 1 0.375
Caucasians T vs. C Random 91.87 0 1.253 0.500-3.138 0.481 0.63 NA NA
TT vs. CC Random 89.45 0.002 1.484 0.188-11.730 0.374 0.708 NA NA
TC vs. CC Fixed 45.46 0.176 1.234 0.873-1.743 1.193 0.233 NA NA
TT+TC vs. CC Random 83.94 0.013 1.248 0.551-2.826 0.532 0.595 NA NA
TT vs. TC+CC Random 88.24 0.004 1.425 0.210-9.655 0.363 0.716 NA NA
MTHFR rs1801133
Overall T vs. C Random 73.78 ≤0.001 1.132 0.956-1.341 1.434 0.151 0.62 0.232
TT vs. CC Random 49.57 0.013 1.212 0.924-1.590 1.388 0.165 0.964 0.802
TC vs. CC Random 77.11 ≤0.001 0.985 0.755-1.284 -0.113 0.91 0.843 0.438
TT+TC vs. CC Random 79.6 ≤0.001 1.095 0.842-1.423 0.677 0.498 0.752 0.215
TT vs. TC+CC Random 83.79 ≤0.001 1.41 0.913-2.176 1.551 0.121 0.62 0.867
Ethnicity
Caucasians T vs. C Fixed 45.38 0.16 1.071 0.891-1.287 0.733 0.464 1 0.431
TT vs. CC Fixed 8.4 0.336 0.996 0.637-1.559 -0.016 0.987 1 0.439
TC vs. CC Fixed 27.78 0.25 1.197 0.930-1.540 1.393 0.164 1 0.413
TT+TC vs. CC Fixed 44.89 0.163 1.162 0.912-1.480 1.214 0.225 1 0.406
TT vs. TC+CC Fixed 0 0.573 0.913 0.594-1.403 -0.415 0.678 1 0.47
Asians T vs. C Random 77.58 ≤0.001 1.173 0.958-1.438 1.542 0.123 0.427 0.119
TT vs. CC Random 55.31 0.008 1.295 0.944-1.776 1.602 0.109 0.854 0.53
TC vs. CC Random 79.26 ≤0.001 0.967 0.705-1.325 -0.211 0.833 0.945 0.264
TT+TC vs. CC Random 82.2 ≤0.001 1.119 0.816-1.532 0.697 0.486 0.427 0.116
TT vs. TC+CC Random 86.09 ≤0.001 1.594 0.961-2.642 1.806 0.071 0.582 0.924
IL-12B rs3212227
Overall C vs. A Random 81.69 0.001 1.076 0.783-1.479 0.452 0.651 0.308 0.236
CC vs. AA Random 54.75 0.085 1.33 0.988-1.790 1.878 0.06 0.734 0.782
CA vs. AA Random 82.52 0.001 1.119 0.696-1.800 0.463 0.643 0.308 0.356
CC+CA vs. AA Random 83.68 ≤0.001 1.125 0.704-1.799 0.492 0.623 0.308 0.317
CC vs. CA+AA Random 24.76 0.263 1.14 0.874-1.487 0.969 0.333 0.734 0.946
Asians C vs. A Fixed 0 0.87 1.166 0.988-1.377 1.812 0.07 NA NA
CC vs. AA Fixed 0 0.932 1.323 0.941-1.860 1.61 0.107 NA NA
CA vs. AA Fixed 0 0.454 1.349 1.032-1.762 2.191 0.028 NA NA
CC+CA vs. AA Fixed 0 0.594 1.34 1.041-1.725 2.271 0.023 NA NA
CC vs. CA+AA Fixed 0 0.507 1.086 0.807-1.461 0.542 0.588 NA NA
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NA, not applicable

TNF-α rs1800629

The summary for the association of TNF-α rs1800629 polymorphism with cervical cancer risk are shown in Table 3. Pooled data from all eligible studies indicated that the TNF-α rs1800629 polymorphism was associated with cervical cancer risk in overall population under four genetic models i.e., allele (A vs. G: OR = 1.277, 95% CI = 1.104-1.477, P = 0.001), homozygote (AA vs. GG: OR = 1.333, 95% CI = 1.062-1.674, P = 0.013), heterozygote (AG vs. GG: OR = 1.307, 95% CI = 1.064-1.605, P = 0.011), and dominant (AA+AG vs. GG: OR = 1.324, 95% CI = 1.104-1.587, P = 0.002). The subgroup analysis by ethnicity also showed that this polymorphism was associated with cervical cancer in Caucasian women under three genetic models i.e., allele (A vs. G, OR = 1.242, 95% CI = 1.043-1.478, P = 0.015); homozygote (AA vs. GG, OR = 1.586, 95% CI = 1.147-2.193, P = 0.005), and recessive (AA vs. AG+GG, OR = 1.569, 95% CI = 1.137-2.165, P = 0.006) and in African women under two genetic models i.e., heterozygote (AG vs. GG, OR = 1.670, 95% CI = 1.228-2.270, P = 0.001) and dominant (AA+AG vs. GG, OR = 1.453, 95% CI = 1.111-1.902, P = 0.006), but not in Asian women.

MTHFR rs1801133

The summary for the association of MTHFR rs1801133 polymorphism with cervical cancer risk are shown in Table 3. Pooled results showed that the MTHFR rs1801133 polymorphism was not associated with cervical cancer in overall population and by ethnicity.

IL-12B rs3212227

The summary for the association of IL-12B rs3212227 polymorphism with cervical cancer risk are shown in Table 4. Pooled data showed that the IL-12B rs3212227 polymorphism was not associated with cervical cancer globally. Stratified analysis by ethnicity revealed that this polymorphism was associated with cervical cancer in Asian women under two genetic models i.e., heterozygote (CA vs. AA: OR = 1.349, 95% CI 1.032-1.762, p=0.028) and dominant (CC+CA vs. AA: OR = 1.340, 95% CI 1.041-1.725, p =0.023).

Table 4.

Summary Risk Estimates for Association of IL-6 rs1800795, TNF-α rs1800629 and TLR9 rs352140 Polymorphisms with Cervical Cancer Risk

Subgroup Genetic Model Type of Model Heterogeneity Odds Ratio Publication Bias
I2 (%) PH OR 95% CI Ztest POR PBeggs PEggers
IL-6 rs1800795 
Overall C vs. G Random 60 0.02 1.294 1.071-1.564 2.675 0.007 0.763 0.701
CC vs. GG Random 54.07 0.042 1.633 1.059-2.520 2.217 0.027 0.763 0.587
CG vs. GG Random 52.47 0.049 1.232 0.971-1.562 1.718 0.086 0.763 0.728
CC+CG vs. GG Random 53.01 0.047 1.312 1.048-1.643 2.371 0.018 1 0.583
CC vs. CG+GG Fixed 48.97 0.068 1.592 1.268-1.999 3.999 ≤0.001 0.548 0.646
Asians C vs. G Fixed 65.97 0.053 1.395 1.230-1.582 5.195 ≤0.001 1 0.793
CC vs. GG Fixed 56.74 0.099 1.951 1.472-2.584 4.656 ≤0.001 1 0.375
CG vs. GG Fixed 66.54 0.05 1.289 1.077-1.543 2.769 0.006 1 0.613
CC+CG vs. GG Fixed 61 0.077 1.429 1.207-1.692 4.136 ≤0.001 1 0.766
CC vs. CG+GG Fixed 53.87 0.114 1.736 1.339-2.251 4.166 ≤0.001 0.296 0.205
TNF-α rs1800629 
Overall A vs. G Random 61.94 ≤0.001 1.277 1.104-1.477 3.291 0.001 0.029 0.025
AA vs. GG Fixed 27.43 0.125 1.333 1.062-1.674 2.481 0.013 0.314 0.366
AG vs. GG Random 70.89 ≤0.001 1.307 1.064-1.605 2.552 0.011 0.183 0.141
AA+AG vs. GG Random 67.34 ≤0.001 1.324 1.104-1.587 3.03 0.002 0.097 0.056
AA vs. AG+GG Fixed 35.98 0.056 1.221 0.977-1.525 1.758 0.079 0.537 0.336
Asians A vs. G Random 78.48 ≤0.001 1.403 0.970-2.029 1.798 0.072 0.035 0.062
AA vs. GG Fixed 43.54 0.088 1.089 0.670-1.770 0.343 0.731 1 0.54
AG vs. GG Random 82.21 ≤0.001 1.469 0.895-2.411 1.521 0.128 0.173 0.267
AA+AG vs. GG Random 81.63 ≤0.001 1.5 0.954-2.359 1.756 0.079 0.173 0.121
AA vs. AG+GG Random 50.71 0.048 1.04 0.487-2.217 0.1 0.92 0.901 0.647
Africans A vs. G Fixed 0 0.786 1.234 0.996-1.529 1.925 0.054 1 0.739
AA vs. GG Fixed 0 0.537 1.156 0.757-1.766 0.672 0.502 1 0.289
AG vs. GG Fixed 24.821 0.264 1.67 1.228-2.270 3.268 0.001 1 0.564
AA+AG vs. GG Fixed 0 0.585 1.453 1.111-1.902 2.725 0.006 1 0.766
AA vs. AG+GG Fixed 0 0.702 0.955 0.640-1.425 -0.225 0.822 1 0.185
Caucasians A vs. G Random 52.45 0.032 1.242 1.043-1.478 2.438 0.015 0.754 0.203
AA vs. GG Fixed 22.58 0.242 1.586 1.147-2.193 2.791 0.005 0.175 0.072
AG vs. GG Random 54.87 0.023 1.123 0.905-1.395 1.056 0.291 0.754 0.906
AA+AG vs. GG Random 52.8 0.031 1.201 0.982-1.469 1.787 0.074 0.916 0.501
AA vs. AG+GG Fixed 22.15 0.246 1.569 1.137-2.165 2.744 0.006 0.348 0.079
TLR9 rs352140
Overall A vs. G Random 84.19 ≤0.001 1.231 0.946-1.603 1.545 0.122 0.763 0.892
AA vs. GG Random 77.72 ≤0.001 1.341 0.834-2.154 1.211 0.226 0.548 0.773
AG vs. GG Random 57.65 0.028 1.236 0.962-1.588 1.658 0.097 1 0.925
AA+AG vs. GG Random 72.99 0.001 1.299 0.967-1.745 1.74 0.082 1 0.939
AA vs. AG+GG Random 76.74 ≤0.001 1.226 0.810-1.856 0.962 0.336 0.763 0.965
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IL-6 rs1800795

The summary for the association of IL-6 rs1800795 polymorphism with cervical cancer risk are shown in Table 4. Pooled data from all eligible studies showed that the IL-6 rs1800795 polymorphism was significantly associated with cervical cancer risk under four genetic models, i.e., allele (C vs. G: OR = 1.294, 95% CI 1.071-1.564, p= 0.007), homozygote (CC vs. GG: OR = 1.633, 95% CI 1.059-2.520, p= 0.027), dominant (CC+CG vs. GG: OR = 1.312, 95% CI 1.048-1.643, p= 0.018) and recessive (CC vs. CG+GG: OR = 1.592, 95% CI 1.268-1.999, p≤0.001). Moreover, subgroup analysis by ethnicity revealed an increased risk of cervical cancer in Asian women.

TLR9 rs352140

The summary for the association of TLR9 rs352140 polymorphism with cervical cancer risk are shown in Table 4. Pooled results showed that the TLR9 rs352140 polymorphism was not associated with cervical cancer in overall population and by ethnicity.

Test of heterogeneity

In the current meta-analysis the χ2 test and I2 statistics were used for assessing the heterogeneity of the included studies. Results indicate that there was statistical heterogeneity for XRCC3 rs861539, MTHFR rs1801133, IL-6 rs1800795, IL-12B rs3212227, TNF-α rs1800629, and TLR9 rs352140 polymorphisms under most genetic models. Thus, the random effect model was used for evaluating the pooled OR and 95% CI for those models. Subgroup analyses showed that ethnicity of participants might contribute to part of heterogeneity.

Sensitivity Analysis

The process of performing a meta-analysis involves a sequence of decisions and it is important to perform a sensitivity analysis in order to assess the impact of different decisions on pooled data. Thus, a sensitivity analysis was carried out by excluding a single study in turn on pooled ORs. The results showed that no individual study had an influence on the pooled OR all involved polymorphisms at XRCC3 rs861539, MTHFR rs1801133, IL-6 rs1800795, IL-12B rs3212227, TNF-α rs1800629, and TLR9 rs352140 polymorphisms, suggesting the stability of our findings. Moreover, excluding HWE deviated studies suggested that there were no independent studies that significantly influenced our pooled data.

Publication Bias

Begg’s funnel plot and Egger’s test were used for evaluating publication bias. As shown in Tables 3 and 4, the Egger’s test results showed that there was no publication bias for the XRCC3 rs861539, MTHFR rs1801133, IL-6 rs1800795, IL-12B rs3212227, TNF-α rs1800629, and TLR9 rs352140 polymorphisms under all five genetic models. Moreover, Begg’s funnel did not statistically revealed a significant publication bias in any of the models for all involved polymorphisms. Thus, the publication bias tests revealed that our pooled ORs were reliable.

Discussion

Genetic factors have been shown to influence the susceptibility of patients to various diseases and have attracted increasing attention (Motamedi et al., 2012; Mazaheri et al., 2014; Kabiri Rad et al., 2018). Several efforts have been made to identify the genetic susceptibility factors underlying development of cervical cancer. However, only a few polymorphisms have shown consistency among studies. In this meta-analysis, the association of XRCC3 rs861539, MTHFR rs1801133, IL-6 rs1800795, IL-12B rs3212227, TNF-α rs1800629 and TLR9 rs352140 polymorphisms with susceptibility to cervical carcinoma was assessed by including all relevant studies.

Yuan et al., (2021) in a meta-analysis based on 15 studies with 5,740 cases and 9,931 controls revealed that there was no significant association between the XRCC3 Thr241Met and the risk of gynecological malignancies. However, their subgroup analysis by ethnicity showed that XRCC3 Thr241Met was associated with an increased risk of gynecological malignancies in Asians. Moreover, their stratified analysis by cancer type indicated that XRCC3 Thr241Met associated with cervical cancer in Asians (CT vs. CC: OR=1.50, 95%CI=1.04-2.14; TT vs. CC: OR=3.14, 95%CI=1.38-7.14; CT+TT vs. CC: OR=1.64, 95% CI=1.17-2.31). Abbas et al., (2010) in a case-control study with 260 cervical cancer cases and 265 controls evaluated the association of XRCC1+399A/G, XRCC2+31467G/A and XRCC3+18067C/T polymorphisms with cervical cancer in Indian women. Their results showed that XRCC2+31479G/A and XRCC3+18067C/T polymorphisms were not associated with cervical cancer. However, they showed that he XRCC1+399A/G is linked with cervical cancer in the Indian population. Al-Harbi et al., (2017) in a study among 232 cervical cancer cases and 313 control subjects evaluated the association of CDKN1A C31A, ATM G1853A, HDM2 T309G, TGFB1 T10C, XRCC1 G399A, and XRCC3 C241T with cervical cancer among Saudi Arabian women. They showed that the TGFB1 T10C and XRCC1 G399A polymorphisms were associated with cervical cancer risk.

Our pooled results showed that the MTHFR rs1801133 polymorphism was not associated with cervical cancer in overall population and by ethnicity. Silva et al., (2019)reported that there were no differences in the genotypic and allelic distribution of MTHFR C677T polymorphism between remission (with the presence of pre-neoplastic lesions) and Persistence (with the presence of pre-neoplastic lesions). The same authors, in another study showed that MTHFR C677T polymorphism was not associated with cervical cancer and HPV infection (Silva et al., 2019). However, Sohrabi et al., in a case-control study evaluated the association of MTHFR A1298C and C677T variants among in 50 cervical intraepithelial neoplasia cases, 98 HPV-positive subjects and 47 non-cancerous/non-HPV patients as healthy controls. Their results showed that MTHFR 1298 CC is more likely to be a potential risk factor for HPV-cervical cancer progression (Sohrabi et al., 2020). Zhou et al., (2020) indicated that the MTHFR rs4846048 enhanced the risk of cervical cancer through association with miR-522. Gong et al., (2018) reported that MTHFR C677T polymorphism was not associated with the risk of cervical cancer or cervical intra-epithelial neoplasia, while, the MTHFR A1298C polymorphism could increase the risk of both cervical cancer and cervical intra-epithelial neoplasia.

In the current study pooled data indicated that the TNF-α rs1800629 polymorphism was associated with cervical cancer risk. Wang et al., (2012) in meta-analysis based 27 studies showed that both TNF-α -238 and -308 G/A polymorphisms could be used to identity individual with elevated susceptibility to cervical cancer in by ethnicity (Bi, 2014). Behboodi et al., (2021) in a study among 153 Iranian cervical cancer cases and 292 free cancer subjects demonstrated that TNF-α rs1800629 was associated with increased level and risk of developing cervical cancer. However, Duvlis et al., (2020) reported that TNF-a-238G/A and TNF-a-308 G/T polymorphisms were not associated with the risk of HPV associated cervical intraepithelial lesions or cervical cancer cases in Macedonian women compared to controls. Moreover, Traore et al., (2020) revealed that TNF-308 G/A or IL18-607C/A polymorphisms were not associated with HPV infection among Burkina Faso women.

Pooled data from all eligible studies showed that the IL-6 rs1800795 polymorphism was significantly associated with cervical cancer risk in overall population and among Asian women. Similarly, Duan et al., (2018) in a meta-analysis based on 7 studies showed that the IL-6 rs1800795 polymorphism is associated with risk of cervical cancer in overall. Pu et al., (2016) in a study with 360 cervical cancer cases and 728 healthy subjects showed that this polymorphism is risk factor for cervical cancer development in Chinese women. de Souza et al., (2006) in a case-control study with 56 cases and 253 controls evaluated the association of IL-6 rs1800795 polymorphism with cervical cancer in a Brazilian population. In 2017, Zidi et al., (2017) in a case-control study evaluated the effects of six different genetic variants atIL-6 with risk of cervical cancer. The study revealed that the IL-6 rs1800795 polymorphism has a has a protective role in cervical cancer development in Tunisian women.de Lima Júnior et al., (2016) revealed that the IL-6 rs1800795 polymorphism was not associated with HPV infection and healthy controls in in Brazilian women. The current meta-analysis data showed that the IL-12B rs3212227 genetic variant was not associated with susceptibility to cervical cancer, while; stratified analysis showed that this polymorphism was associated with cervical cancer risk among Asian women. Zheng et al., (2017) in a meta-analysis based on 33 articles with 10,587 cancer cases and 12,040 healthy subjects assessed the genetic association of IL-12B rs3212227 with cancer risk. Their pooled data indicated that IL-12B rs3212227 polymorphism was associated with cancer risk in overall. However, subgroup analysis by cancer type showed that IL-12B rs3212227 was not associated with cervical cancer. In 2016, Chang et al., (2015) in a meta-analysis based on 5 studies with 2552 cervical cases and 2232 healthy subjects showed that this polymorphism did not associate with risk of cervical cancer. In 2012, de Carvalho et al., found that the IL-12B rs3212227 variant has a protective role in development of cervical cancer in Brazilian women (Do Carmo Vasconcelos De Carvalho et al., 2012). Similarly, Han et al., (2008) found that IL-12B rs3212227 did not associate with cervical cancer risk in Korean population. However, Roszak et al., (2012) showed that this polymorphism was associated with increased risk of cervical cancer among polish women.

The current meta-analysis data showed that TLR9 rs352140 was not associated with susceptibility to cervical cancer. Nath et al., (2020) in a study showed that TLR4/9 polymorphisms are associated with increased HPV16/18 infection susceptibility and cervical squamous cell carcinoma risk among the women of Jharkhand state. In another study in India showed the TLR4 and TLR9 polymorphisms and haplotypes with hrHPV infection and cervical cancer risk (Pandey et al., 2019). Martínez-Campos et al., (2017) in a case-control study revealed that TLR9 rs187084 is a risk factor for HPV infection, squamous intraepithelial cervical lesion, and uterine cervical neoplasm in Mexican female population. Jin et al., (2017) also evaluated the association of some variants at Toll-like receptors gene with cervical cancer among 420 Chinese cervical cancer patients and 842 controls. Their results showed that mutant alleles of TLR2 rs3775290, TLR4 rs7873784, and TLR9 rs352140 were associated with increased cervical cancer risk. Similarly, Yang et al., (2020) in meta-analysis based on eleven studies indicated that the TLR9 rs187084 and rs352140 polymorphisms may contribute to development of cervical cancer, but not TLR2-196 to -174 del/ins polymorphism.

In summary, our pooled data indicated that the XRCC3 RS861539, TNF-α rs1800629, and IL-6 rs1800795 genetic variants were associated with susceptibility to cervical cancer globally. However, the MTHFR rs1801133, IL-12B rs3212227 and TLR9 rs352140 variants were not associated. Larger and more rigorous studies among different ethnicities are needed to further evaluate these associations with cervical cancer.

Author Contribution Statement

Seyedeh Fatemeh Parsaeian, Fatemeh Asadian, Mojgan Karimi-Zarchi: conceptualization, investigation. Seyed Alireza Dastgheib, Sepideh Setayesh: Software, original draft preparation. Atiyeh Javaheri, Razieh Sadat Tabatabaie: Investigation. Fatemeh Asadian: Investigation, writing. Fatemeh Asadian, Hossein Neamatzadeh: Methodology, software. Hossein Golestanpour, Hossein Neamatzadeh: Formal analysis, investigation. Hossein Golestanpour: Project administration. Atiyeh Javaheri, Razieh Sadat Tabatabaie: Writing, reviewing, editing.

Ethics approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Consent to participate

Not applicable for this manuscript.

Availability of data and material

The datasets generated during and/or analyzed during this study are available from the corresponding author on reasonable request.

Conflicts of interest

The authors declare that they have no conflict of interest.

Acknowledgements

None.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The datasets generated during and/or analyzed during this study are available from the corresponding author on reasonable request.

Articles from Asian Pacific Journal of Cancer Prevention : APJCP are provided here courtesy of West Asia Organization for Cancer Prevention

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